检索结果-内蒙古大学图书馆

15th International Conference on Web Information Systems Engineering (WISE)

作者： Zhong, MingJie Ding, ZhiJun Sun, HaiChun Wang, PengWei Tongji Univ Minist Educ Key Lab Embedded Syst & Serv Comp Shanghai 200092 Peoples R China Univ Pisa Dept Comp Sci I-56127 Pisa Italy

ISBN: (纸本)9783319117492;9783319117485

This paper presents a novel clustering algorithm based on clustering coefficient. It includes two steps: First, k-nearest-neighbor method and correlation convergence are employed for a preliminary clustering. Then, the results are further split and merged according to intra-class and inter-class concentration degree based on clustering coefficient. The proposed method takes correlation between each other in a cluster into account, thereby improving the weakness existed in previous methods that consider only the correlation with center or core data element. Experiments show that our algorithm performs better in clustering compact data elements as well as forming some irregular shape clusters. It is more suitable for applications with little prior knowledge, e. g. hotspots discovery.

关键词： clustering algorithm clustering coefficient self-learning clustering

来源：评论

学校读者我要写书评

暂无评论

Study on online English learning resource push based on Bayesian inference

引用

INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2024年第1-2期74卷 90-98页

作者： Zhang, Wenfeng Jiujiang Univ Sch Foreign Languages Jiujiang Jiangxi Peoples R China

The current online English learning resource push methods have problems of poor customer satisfaction, low reliability of pushed resources and low recall rate of resource pushes. Therefore, this paper proposes an online English learning resource push method based on Bayesian inference. Firstly, obtain online English learning resource data and classify online learning resources. Then, by mining and analysing learner learning data, clustering algorithms are used to locate and infer the learner's learning style. Finally, based on Bayesian inference, a Naive Bayesian classifier is developed, and a network English online learning resource push model is developed to achieve effective network English online learning resource distribution. Through relevant experiments, it has been confirmed that the customer satisfaction of this method varies from 96.0% to 99.8%, the push reliability varies from 90.5% to 99.8% and the resource push recall rate is 99.9%, which has the characteristic of good push effect.

关键词： Bayesian inference online English learning resource push classify clustering algorithm Naive Bayesian classifier

来源：评论

学校读者我要写书评

暂无评论

Pointwise norm-based clustering of data in arbitrary dimensional space

引用

Communications in Statistics Case Studies Data Analysis and Applications 2023年第2期9卷 121-134页

作者： Modak, Soumita Faculty Department of Statistics University of Calcutta Basanti Devi College Kolkata India

In this paper, a novel nonparametric norm-based clustering algorithm is proposed to classify real-valued continuous data sets given in arbitrary dimensional space. For data univariate, multivariate or high-dimensional, with the number of study variables close to or larger than the data size, our straightforward algorithm is implemented throughout under an univariate set-up, where we make use of the observation-wise (or pointwise) norms which quantify the distances of the observations from the origin zero or the null vector. The method begins with determination of the sample quantile using nonparamteric bootstrapping on the computed norms and always converges independently. By its design, the suggested algorithm is fast enough to detect the number of existing clusters itself and to form well-defined groups. Data study demonstrates its competitiveness in comparison to 2 popular clustering algorithms K-means and K-medoids. © 2023 Taylor & Francis Group, LLC.

关键词： arbitrary-dimensional data set bootstrapping clustering algorithm high-dimensional applicability nonparametric method pointwise norm

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing education: Harnessing data mining for enhanced resource repository in fundamental training programs

引用

JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年第1期25卷 341-354页

作者： Wu, Xiaozhou Guizhou Univ Commerce Phys Educ Dept Twenty Sixth Ave Guiyang 550014 Peoples R China

As information technology and data mining techniques evolve at a breakneck pace, they bring transformative potential to the educational landscape. The burgeoning growth of online educational resources not only enriches learning but also introduces complexities in resource management and personalization. This study pioneers the construction of a Fundamental Training Educational Resource Repository (FTERR) by harnessing sophisticated data mining approaches. We initiate by intricately mapping students' learning needs through knowledge graph and semantic analysis, ensuring a deep alignment with individual educational journeys. Subsequently, we address the hurdles in resource management by introducing an innovative data storage model. This model, in synergy with advanced clustering algorithms, streamlines the retrieval process, rendering it both swift and intuitive. Our research transcends traditional data mining applications in education, steering towards a more informed and responsive educational ecosystem. This novel approach not only elevates the precision and efficiency of educational resource allocation but also significantly enriches the student learning experience, marking a leap forward in educational informatization. The main purpose of this study is to address the issue of insufficient personalized learning needs in online educational resources by introducing advanced data mining technologies, including knowledge graphs and semantic analysis. The study develops new data storage models and utilizes clustering algorithms to achieve rapid and visual retrieval of educational resources, thereby enhancing the utilization efficiency of educational resources and improving students' learning experiences. The methods and findings of this study can provide references for data mining applications in other fields and promote the application and development of data science in a broader range of fields.

关键词： data mining Fundamental Training Educational Resource Repository (FTERR) knowledge graph semantic analysis resource recommendation clustering algorithm educational informatization

来源：评论

学校读者我要写书评

暂无评论

A clustering algorithm Applying to Transportation Market Segment

A Clustering Algorithm Applying to Transportation Market Seg...

引用

3rd International Conference on Civil Engineering and Transportation (ICCET 2013)

作者： Jing, Chongyi Sun, Hong Zhang, Peiwen Civil Aviat Flight Univ China Air Transportat Management Sch Guanghan 618307 Sichuan Peoples R China

ISBN: (纸本)9783038350064

An integration algorithm for clustering is presented, in which a maximization & minimum algorithm to determine the initial centers and BWP (Between-Within Proportion) index for input of optimal k. In theory, the bigger the BWP index, the better the clustering effectiveness. Then a numerical example of air transport market segment is presented to show the effectiveness and efficiency of the method presented in the document.

关键词： clustering algorithm Between-Within Proportion K-Means algorithm Transportation Market Segment

来源：评论

学校读者我要写书评

暂无评论

Fuzzy clustering of structural members for efficient design optimization

引用

Asian Journal of Civil Engineering 2023年第3期24卷 711-725页

作者： Patil, Ishwaragouda S. Joshi, Sharad G. Patil, Shabarish V. Department of Civil Engineering KLE Institute of Technology Hubballi 580027 India KLE Institute of Technology Hubballi 580027 India

The basis for grouping structural elements is a tradeoff between optimization in design and construction. The grouped elements should have common optimal design specifications that are structurally safe and convenient for construction. Fuzzy clustering provides flexibility to regroup the elements using membership values indicating their affinity to a cluster for the considered parameter. The proposed Fuzzy C-Means algorithm demonstrates grouping efficacy based on a dominant design parameter and also provides leverage for regrouping of an element considering other design or construction aspects. The method is evaluated by applying it to footings of four buildings with varying complexity, taking load on footing as basis for grouping. Distinct and homogeneous clusters are obtained with small computational effort, with further scope for regrouping members based on other parameters using their membership values for the parameter considered. The paper illustrates various examples for grouping and optimizing structural design. The proposed methodology proved to be very efficient in grouping structural elements that optimizes the overall structural design. In case of truss example nearly 7% reduction in weight was found compared to previous research. Optimization methodology is also proposed for rebar design of Reinforced Cement Concrete structures with fixed topology and cross-sectional dimensions. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Automated grouping clustering algorithm Fuzzy C-means clustering Narrow design search Structural optimization

来源：评论

学校读者我要写书评

暂无评论

A Geometric clustering algorithm and Its Applications to Structural Data

引用

18th Annual International Conference on Research in Computational Molecular Biology (RECOMB)

作者： Xu, Shutan Zou, Shuxue Wang, Lincong Jilin Univ Coll Comp Sci & Technol Changchun 130023 Jilin Peoples R China

ISBN: (纸本)9783319052694;9783319052687

An important feature of structural data especially those from structural determination and protein-ligand docking programs is that their distribution could be both uniform and non-uniform. Traditional clustering algorithms developed specifically for non-uniformly distributed data may not be adequate for their classification. Here we present a geometric partitional algorithm that could be applied to both uniformly and non-uniformly distributed data. The algorithm is a top-down approach that recursively selects the outliers as the seeds to form new clusters until all the structures within a cluster satisfy certain requirements. The applications of the algorithm to a diverse set of data from NMR structure determination, protein-ligand docking and simulation show that it is superior to the previous clustering algorithms for the identification of the correct but minor clusters. The algorithm should be useful for the identification of correct docking poses and for speeding up an iterative process widely used in NMR structure determination.

关键词： clustering algorithm structure determination protein-ligand docking structure classification

来源：评论

学校读者我要写书评

暂无评论

A point symmetry clustering algorithm based on PSO

A point symmetry clustering algorithm based on PSO

引用

International Conference of Information Science and Management Engineering (ISME)

作者： Qu, Jianhua Shandong Normal Univ Sch Management Sci & Engn Jinan Peoples R China

ISBN: (纸本)9781845648299;9781845648282

This paper proposed an efficient PSO clustering algorithm with point symmetry distance based on cooperative evolution strategy. It not only determined the number of clusters, but also detected the proper partitions in data sets when the data sets possess the property of symmetry. In the algorithm, a new point symmetry distance is used to compute the similarity instead of the Euclid distance. Cooperative evolution strategy with multi-populations is introduced to prevent the PSO algorithm from trapping into the local optimal solution. The performance of the proposed algorithm is tested in two artificial data sets. The simulation results show that the performance of the algorithm is better than other algorithms mentioned in this paper.

关键词： clustering algorithm cooperative evolution point symmetry distance PSO

来源：评论

学校读者我要写书评

暂无评论

Power backup Density based clustering algorithm for Maximizing Lifetime of Wireless Sensor Networks 4

Power backup Density based Clustering Algorithm for Maximizi...

引用

4th International Conference on Wireless Communications, Vehicular Technology, Information Theory and Aerospace & Electronic Systems (VITAE)

作者： Wagh, Sanjeev Prasad, Ramjee Univ Pune KJ Coll Engn & Mgmt Res Pune Maharashtra India Aalborg Univ Ctr TeleInfrastruktur CTIF Aalborg Denmark

ISBN: (纸本)9781479946235

WSNs consists several nodes spread over experimental fields for specific application temporarily. The spatially distributed sensor nodes sense and gather the information for intended parameters like temperature, sound, vibrations, etc for the particular application. In this paper, we evaluate the impact of different algorithms i.e. clustering for densely populated field application, energy backup by adding energy harvesting node in field, positioning energy harvesting node smartly in the field and also positioning the base station in sensor field to optimize the communication between cluster head and base station. The analysis and simulation results justifies that availability of power backup for cluster nodes using energy harvesting and positioning the energy harvesting node and also base station enhance the lifetime of sensor network fields. WSN with power backup density based clustering algorithm can be applied for many sensitive applications like military for hostile and remote areas or environmental monitoring where human intervention is not possible.

关键词： Wireless sensor networks clustering algorithm Network lifetime Energy harvesting nodes

来源：评论

学校读者我要写书评

暂无评论

Indoor personnel detection and tracking of millimeter-wave radar based on improved DBSCAN algorithm

ENGINEERING RESEARCH EXPRESS

引用

ENGINEERING RESEARCH EXPRESS 2025年第2期7卷 025220-025220页

作者： Zhou, Fang Gao, Yuan Li, Andong Xing, Mengdao Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230009 Peoples R China Univ Birmingham Sch Engn Birmingham B15 2TT England Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

With the progress of technology and the enhancement of social demand for privacy protection, optical monitoring equipment has gradually caused public concern. In contrast, millimeter-wave(mmWave) radar monitoring has been rapidly developed because of its superiority in privacy. However, the indoor environment is relatively complex, and traditional density-based clustering algorithms perform poorly in accurate tracking. The point cloud data generated from indoor scenario echoes collected by mmWave radar is relatively sparse and accompanied by noise points, which significantly affects tracking performance. In this paper, we propose an improved DBSCAN clustering algorithm that uses a multi-frame aggregation method to suppress multipath effects and eliminate 'false targets'. It is combined with the extended Kalman filter(EKF) algorithm to form a complete system. In our system, the raw data collected by mmWave radar is processed by fast fourier transform(FFT), static clutter removal and constant false alarm rate(CFAR) to obtain point cloud data. Since the density of point cloud data greatly affects the performance of clustering algorithms, we use multi-frame aggregation method to process the point cloud data to increase its density. Accurate indoor personnel tracking is then achieved through clustering and extended Kalman filtering, and the tracking error is within 0.1 m.

关键词： mmWave radar clustering algorithm multi-frame aggregation indoor tracking

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：